Leaving No Blind Spots: Toward Automotive Cybersecurity

The increasing connectivity and autonomy of modern vehicles have drastically expanded their attack surface, introducing interdependent cybersecurity risks. However, existing security mechanisms often focus on isolated threats, failing to address their interplay within complex vehicle ecosystems. As vehicles become increasingly dependent on AI-driven control, electric powertrains, and networked architectures, ensuring resilience across multiple attack vectors requires a holistic security approach. This work proposes a unified three-layer security framework that integrates (i) physical-layer protection through battery authentication and side-channel resilience, (ii) AI-layer robustness against adversarial attacks on perception and intrusion detection, and (iii) communication-layer security for in-vehicle network protection. By leveraging cross-domain security principles, including cyber-physical security analysis, adversarial ML defenses, and in-vehicle network protection, this framework provides a cohesive and scalable methodology for securing next-generation automotive systems.